Technical Implementation of a Multi-component, Text Message–Based Intervention for Persons Living with HIV
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men (MSM) continue to be severely and disproportionately affected by the HIV/AIDS (human immunodeficiency virus/acquired immune deficiency syndrome) epidemic in the United States. Effective antiretroviral therapy has altered the HIV epidemic from being an acute disease to a chronic, manageable condition for many people living with HIV. The pervasiveness, low cost, and convenience of Short Message Service (SMS) suggests its potential suitability for supporting the treatment of conditions that must be managed over an extended period. OBJECTIVE: The purpose of this proof-of-concept study was to develop, implement, and test a tailored SMS-based intervention for HIV-positive MSM. Prior studies do not routinely provide sufficiently detailed descriptions of their technical implementations, restricting the ability of subsequent efforts to reproduce successful interventions. This article attempts to fill this gap by providing a detailed description of the implementation of an SMS-based intervention to provide tailored health communication messages for HIV-positive MSM. METHODS: We used archives from the SMS system, including participant responses to messages and questions sent via SMS, as the data sources for results reported in this article. Consistent with the purpose of this article, our analysis was limited to basic descriptive statistics, including frequency distributions, means and standard deviations. RESULTS: During the implementation period, we sent a total of 7,194 messages to study participants, received 705 SMS responses to our two-way SMS questions of participants, and 317 unprompted SMS message acknowledgements from participants. Ninety two percent of participants on antiretroviral therapy (ART) responded to at least one of the weekly medication adherence questions administered via SMS, and 27% of those had their medication adherence messages changed over the course of the study based on their answers to the weekly questions. Participants who responded to items administered via SMS to assess satisfaction with and use of the messages reported generally positive perceptions, although response rates were low overall. CONCLUSIONS: Results confirm the technical feasibility of deploying a dynamically tailored, SMS-based intervention designed to provide ongoing behavioral reinforcement for HIV-positive MSM. Lessons learned related to text programming, message delivery and study logistics will be helpful to others planning and implementing similar interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".